Comparative Evaluation of Data Mining Algorithms in Breast Cancer
Source abstract
Abstract
Unchecked breast cell growth is one of the leading causes of death in women globally and is the cause of breast cancer. The only method to avoid breast cancer-related deaths is through early detection and treatment. The proper classification of malignancies is one of the most significant challenges in the medical industry. Due to their high precision and accuracy, machine learning techniques are extensively employed for identifying and classifying various forms of cancer. Several data mining algorithms were studied and implemented by the author of this review and compared them to the present parameters and accuracy of various algorithms for breast cancer diagnosis such that clinicians might use them to accurately detect cancer cells early on. This article introduces several techniques, including support vector machine (SVM), K star (K*) classifier, Additive Regression (AR), Back Propagation Neural Network (BP), and Bagging. These algorithms are trained using a set of data that contains tumor parameters from breast cancer patients. Comparing the results, the author found that Support Vector Machine and Bagging had the highest precision and accuracy, respectively. Also, assess the number of studies that provide machine learning techniques for breast cancer detection.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Artificial Intelligence in Healthcare · AI in cancer detection · Machine Learning and Data Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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